Hasil untuk "Special industries and trades"

K.J. Rajimon, Rajiv Gandhi Gopalsamy

Perovskite, oxide, organic, and dye-sensitised solar cells are studied from 2015 to 2025, and their current standing and future Mott-Schottky (MS) analysis in photovoltaic (PV) research are highlighted in this review. The incorporation of MS characterisation methodology with solar cell capacitance simulator one dimension (SCAPS-1D) simulations, ab-initio calculations, impedance spectroscopy, and nascent data-driven models is addressed. The MS approach will always be at the forefront in the extraction of the flat band potential, doping concentration, depletion region width, and built-in potential. This is the link between the energetics of the semiconductors and the charge transport of the solar cells and other PV. With MS-validated doping profile optimisation, interface engineering achieves (37.66%) power conversion efficiencies, 1.52 V (open-circuit voltages) and fill factors above (87%). Unfortunately, there are limitations of the frequency-dependent capacitance, parasitic elements, trap states, and non-ideal depletion layer of some architectures, like organic and hybrid ones. The MS and simulations to be used together, and machine learning adoption and analytical models to improve the electronic characterisation, have the potential to resolve the problems. This study offers a critical evaluation of current methods and inherent constraints in MS analysis, offering a strategic framework for the systematic design of efficient, durable, and sustainable solar technologies.

Emanuele Coccia, Martino Bernasconi, Andrea Celli

We study the problem of contextual online bilateral trade. At each round, the learner faces a seller-buyer pair and must propose a trade price without observing their private valuations for the item being sold. The goal of the learner is to post prices to facilitate trades between the two parties. Before posting a price, the learner observes a $d$-dimensional context vector that influences the agent's valuations. Prior work in the contextual setting has focused on linear models. In this work, we tackle a general nonparametric setting in which the buyer's and seller's valuations behave according to arbitrary Lipschitz functions of the context. We design an algorithm that leverages contextual information through a hierarchical tree construction and guarantees regret $\widetilde{O}(T^{{(d-1)}/d})$. Remarkably, our algorithm operates under two stringent features of the setting: (1) one-bit feedback, where the learner only observes whether a trade occurred or not, and (2) strong budget balance, where the learner cannot subsidize or profit from the market participants. We further provide a matching lower bound in the full-feedback setting, demonstrating the tightness of our regret bound.

Ramil R. Zagidullin, Ivan A. Bannikov

A traffic accident is a phenomenon that has already become an integral part of human life, and although it is often taken for granted, accidents have serious consequences for people, resulting in physical injury, emotional stress, financial loss, and death. In addition, accidents can cause traffic jams, which leads to delays, creating inconveniences for movement. Therefore, the fight against road accidents is an important task around the world. This requires effective measures to prevent and reduce the risk of accidents. First of all, proper planning of the road network contributes to safety, and in order to assess them, it is necessary to substantiate the effectiveness of the proposed measures. One of the methods of substantiating the possibilities of optimizing intersections is computer simulation, which allows not only analyzing, but also predicting the behavior of transport processes. The purpose of the study is to develop practical tools for improving road safety. Methodology. Theoretical and practical research methods were used: system analysis, information synthesis, observations, measurements, experiments and comparisons. Results. The possibilities of practical tools in the formation and development of transport infrastructure are presented. Scenarios for solving the problem of road safety at one of the intersections of the Kazan city road network have been developed. Practical implications. When optimizing intersections, when assessing the risks of deterioration in case of incorrect design, or when evaluating effectiveness, use computer modeling methods. EDN: UKYLKH

X. Liu, G. Wang, J. Si et al.

The open jet flame in hot co-flow (JHC) has been frequently utilized for fundamental investigations of Moderate or Intense Low-oxygen Dilution (MILD) combustion due to its controllable conditions and relatively easy measurement capabilities. However, practical MILD combustion must take place within a combustor that is enclosed. Therefore, it is necessary to examine the similarity and disparity of combustion characteristics between two flame configurations. This issue is addressed currently. Specifically, we investigate the flow mixing, ignition, and combustion, as well as emission characteristics of non-premixed and premixed JHC and cylindrical furnace (FUR) flames at various values of the environmental temperature (Te) and central jet Reynolds number (Re). For the open non-premixed flames, we employ both previous single-tube JHC (SJHC) combustor and presently modified JHC (MJHC) one that uses the same nozzle configuration as the FUR burner. It is revealed that significant differences occur in flow, combustion and emission characteristics between the SJHC and FUR cases. On the other hand, both non-premixed and premixed MJHC configurations exhibit high similarity to the corresponding FUR cases in terms of upstream flow mixing and combustion features. Moreover, different CO and NOx emissions result from open JHC and close furnace flames due to different post-combustion configuration and residence time. Accordingly, future experiments on non-premixed MJHC and premixed JHC flames are highly recommended for better understanding practical MILD combustion.

Tracy Patricia Nindry Abigail Rolnmuch, Yuhana Astuti

The realization of FDI and DDI from January to December 2022 reached Rp1,207.2 trillion. The largest FDI investment realization by sector was led by the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industry sector, followed by the Mining sector and the Electricity, Gas, and Water sector. The uneven amount of FDI investment realization in each industry and the impact of the COVID-19 pandemic in Indonesia are the main issues addressed in this study. This study aims to identify the factors that influence the entry of FDI into industries in Indonesia and measure the extent of these factors' influence on the entry of FDI. In this study, classical assumption tests and hypothesis tests are conducted to investigate whether the research model is robust enough to provide strategic options nationally. Moreover, this study uses the ordinary least squares (OLS) method. The results show that the electricity factor does not influence FDI inflows in the three industries. The Human Development Index (HDI) factor has a significant negative effect on FDI in the Mining Industry and a significant positive effect on FDI in the Basic Metal, Metal Goods, Non-Machinery, and Equipment Industries. However, HDI does not influence FDI in the Electricity, Gas, and Water Industries in Indonesia.

Peyman Khezr

Secondary markets and resale are integral components of all emission trading systems. Despite the justification for these secondary trades, such as unpredictable demand, they may encourage speculation and result in the misallocation of permits. In this paper, our aim is to underscore the importance of efficiency in the initial allocation mechanism and to explore how concerns leading to the establishment of secondary markets, such as uncertain demand, can be addressed through alternative means, such as frequent auctions. We demonstrate that the existence of a secondary market could lead to higher untruthful bids in the auction, further encouraging speculation and the accumulation of rent. Our results suggest that an inefficient initial allocation could enable speculators with no use value for the permits to bid in the auction and subsequently earn rents in secondary markets by trading these permits. Even if the secondary market operates efficiently, the resulting rent, which represents a potential loss of auction revenue, cannot be overlooked.

Carlos Góes

Can trade integration induce product innovation? I document that countries that joined the European Union (EU) started producing more product varieties, investing more in R&D, and trading more compared to candidate countries that did not join at a given horizon. Additionally, I show that a plausibly exogenous increase in market access increases the probability of a given country starting production of and exporting a given product. To rationalize this reduced-form evidence, I propose a new quantitative framework that integrates the forces of specialization and market size. This is a dynamic general equilibrium model of frictional trade and endogenous growth with arbitrarily many asymmetric countries that nests the Eaton-Kortum model of trade and the Romer growth model as special cases. The key result is an analytical expression to decompose gains from trade into dynamic and static components. In this framework, the product innovation growth rate increases with higher market access. Finally, a quantitative version of the model suggests that: (a) the EU enlargement increased its long-run yearly growth rate by about 0.10pp; and (b) dynamic gains can account for between 65-90% of total welfare gains from trade.

Yi Chen, Jun Bin, Chao Kang

As harvesting at the right time is crucial to ensuring the best quality and maximizing the yield of tobacco leaves, great attention has been paid to the research of “harvest maturity”. Fresh tobacco leaves can be manually categorized into four maturity stages, including immature, pseudomature, mature, and hypermature, according to the leaf tissue structure and color. To improve the discriminative accuracy, convenience, and automation of maturity levels, a tobacco leaf maturity discriminative method based on machine vision and deep learning was developed and integrated into a system based on the mobile terminal. In this method, the chromatic features and texture features can be automatically extracted from the acquired digital image of tobacco leaf by convolutional neural networks (CNNs). Experimental results of an independent test set consisting of 480 tobacco leaf samples demonstrated that the proposed system was able to classify the maturity stage of tobacco leaf efficiently and accurately. The system's ability to run on mobile devices, like smartphones, makes it easier to accurately collect mature tobacco leaves and reduces subjectivity compared to the current visual identification approach. Moreover, this system can provide a picking date recommendation to tobacco growers.

Rabiul Armala Sari Nande Putri, Hesti Nur'Aini, Andwini Prasetya et al.

RSU private X Bengkulu is one of general hospital in Bengkulu provides various health services, including outpatient, emergency units, and inpatient care. In the inpatient service unit there is a class 3 treatment room which is an option for patients with a fairly cheap and affordable cost, but the existing facilities are different from other rooms. This study aims to analyze the level of satisfaction and nutritional value of food menus for class 3 patients at RSU private X Bengkulu. The respondents involved were 75 patients who were treated at RSU private X Bengkulu with a descriptive analysis using a Likert scale. The results showed that the level of patient satisfaction with services at RSU private X Bengkulu was 4.08 with the assessment criteria being satisfied from the indicators of satisfaction, responsiveness (responses), confidence (belief), empathy, tangible, reliability, according to diet, food portions, suitability, and cleanliness of equipment. Nutritional Value of Food Grade 3 patients served have a Calorific Value between 1,800.63 Calories/day.

Anis Huzaimanor Izafera, Nur Salam, Dewi Sri Susanti

In Indonesia, the plantation sub-sector has an important role in increasing state revenue through the exports of its products, besides the mining and gas sector. The most widely produced plantation crops in Indonesia are oil palm and rubber and South Kalimantan is one of the top 10 provinces in Indonesia with oil palm plantations. This study aims to detect the correct forecasting model for data on oil palm crops and rubber production in South Kalimantan Province and to analyse the forecasting results for oil palm crops and rubber in South Kalimantan Province using the double exponential smoothing method.  This research was conducted for 8 months (March 2022 to December 2022), using observational data from 2001 to 2021. Double Exponential Smoothing Holt was used in this study by looking at the error value obtained with the smallest Mean Absolute Percentage Error (MAPE). For palm oil production, the parameters α=0.8 and β=0.6 were the best parameters with a MAPE value of 8.05% and resulted in the forecasting of oil palm crops production in 2022 not increasing, in 2023 and 2024 experiencing an increase of 1%. As for forecasting rubber production, the parameters α=0.9 and β=0.9 are the best parameters with a MAPE value of 5.45% and forecasting rubber production in 2022 will increase by 1%, in 2023 and 2024 by 2%.

Jongsub Lee, Hayong Yun

Using deep learning techniques, we introduce a novel measure for production process heterogeneity across industries. For each pair of industries during 1990-2021, we estimate the functional distance between two industries' production processes via deep neural network. Our estimates uncover the underlying factors and weights reflected in the multi-stage production decision tree in each industry. We find that the greater the functional distance between two industries' production processes, the lower are the number of M&As, deal completion rates, announcement returns, and post-M&A survival likelihood. Our results highlight the importance of structural heterogeneity in production technology to firms' business integration decisions.

Razin Farhan Hussain, Mohsen Amini Salehi

Industry 4.0 operates based on IoT devices, sensors, and actuators, transforming the use of computing resources and software solutions in diverse sectors. Various Industry 4.0 latency-sensitive applications function based on machine learning to process sensor data for automation and other industrial activities. Sending sensor data to cloud systems is time consuming and detrimental to the latency constraints of the applications, thus, fog computing is often deployed. Executing these applications across heterogeneous fog systems demonstrates stochastic execution time behavior that affects the task completion time. We investigate and model various Industry 4.0 ML-based applications' stochastic executions and analyze them. Industries like oil and gas are prone to disasters requiring coordination of various latency-sensitive activities. Hence, fog computing resources can get oversubscribed due to the surge in the computing demands during a disaster. We propose federating nearby fog computing systems and forming a fog federation to make remote Industry 4.0 sites resilient against the surge in computing demands. We propose a statistical resource allocation method across fog federation for latency-sensitive tasks. Many of the modern Industry 4.0 applications operate based on a workflow of micro-services that are used alone within an industrial site. As such, industry 4.0 solutions need to be aware of applications' architecture, particularly monolithic vs. micro-service. Therefore, we propose a probability-based resource allocation method that can partition micro-service workflows across fog federation to meet their latency constraints. Another concern in Industry 4.0 is the data privacy of the federated fog. As such, we propose a solution based on federated learning to train industrial ML applications across federated fog systems without compromising the data confidentiality.

Laurie M. Heller, Benjamin Elizalde, Bhiksha Raj et al.

Machine Listening, as usually formalized, attempts to perform a task that is, from our perspective, fundamentally human-performable, and performed by humans. Current automated models of Machine Listening vary from purely data-driven approaches to approaches imitating human systems. In recent years, the most promising approaches have been hybrid in that they have used data-driven approaches informed by models of the perceptual, cognitive, and semantic processes of the human system. Not only does the guidance provided by models of human perception and domain knowledge enable better, and more generalizable Machine Listening, in the converse, the lessons learned from these models may be used to verify or improve our models of human perception themselves. This paper summarizes advances in the development of such hybrid approaches, ranging from Machine Listening models that are informed by models of peripheral (human) auditory processes, to those that employ or derive semantic information encoded in relations between sounds. The research described herein was presented in a special session on "Synergy between human and machine approaches to sound/scene recognition and processing" at the 2023 ICASSP meeting.

Hongyin Zhu

Risk assessment across industries is paramount for ensuring a robust and sustainable economy. While previous studies have relied heavily on official statistics for their accuracy, they often lag behind real-time developments. Addressing this gap, our research endeavors to integrate market microstructure theory with AI technologies to refine industry risk predictions. This paper presents an approach to analyzing industry trends leveraging real-time stock market data and generative small language models (SLMs). By enhancing the timeliness of risk assessments and delving into the influence of non-traditional factors such as market sentiment and investor behavior, we strive to develop a more holistic and dynamic risk assessment model. One of the key challenges lies in the inherent noise in raw data, which can compromise the precision of statistical analyses. Moreover, textual data about industry analysis necessitates a deeper understanding facilitated by pre-trained language models. To tackle these issues, we propose a dual-pronged approach to industry trend analysis: explicit and implicit analysis. For explicit analysis, we employ a hierarchical data analysis methodology that spans the industry and individual listed company levels. This strategic breakdown helps mitigate the impact of data noise, ensuring a more accurate portrayal of industry dynamics. In parallel, we introduce implicit analysis, where we pre-train an SML to interpret industry trends within the context of current news events. This approach leverages the extensive knowledge embedded in the pre-training corpus, enabling a nuanced understanding of industry trends and their underlying drivers. Experimental results based on our proposed methodology demonstrate its effectiveness in delivering robust industry trend analyses, underscoring its potential to revolutionize risk assessment practices across industries.

Anton Ming-Zhi Gao, Tsung Kuang Yeh, Jong-Shun Chen

Taiwan launched an energy transition agenda to pursue a nuclear-free homeland by 2025 after the anti-nuclear party won the 2016 presidential and parliament elections. In 2016, the 2025 electricity mix target was set to 50% gas-fired power, 30% coal-fired power, and 20% renewable electricity (RE), and thus, no nuclear power. Despite many efforts, the electricity mix remained far from these targets at the end of 2020: coal-fired power, 43.5%; gas-fired power, 38%; RE, 7.1%; and nuclear power, 8.5%. This study evaluates the possibility of achieving the 2025 targets and the barriers to reaching each target. It also uses the concept of a ‘just’ energy transition to assess whether this vision meets the related criteria and why.

Kai Xiao, Daoxing Li, Xiaohui Wang et al.

Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems. New energy sources are continuously being connected to distribution grids; this, however, increases the complexity of the information structure of marketing and distribution businesses. The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks. As a solution, this paper presents a data model of “one graph of marketing and distribution” and a framework for graph computing, by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory. Specifically, this work aims to determine the correlation between distribution transformers and marketing users, which is crucial for elucidating the connection between marketing and distribution. In this manner, a novel identification algorithm is proposed based on the collected data for marketing and distribution. Lastly, a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads. Furthermore, an operation and maintenance (O&M) knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.

Siddhartha Paul Tiwari

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.

George Bouzianis, Lane P. Hughston, Leandro Sánchez-Betancourt

We consider a pair of traders in a market where the information available to the second trader is a strict subset of the information available to the first trader. The traders make prices based on the information available concerning a security that pays a random cash flow at a fixed time $T$ in the future. Market information is modelled in line with the scheme of Brody, Hughston & Macrina (2007, 2008) and Brody, Davis, Friedman & Hughston (2009). The risk-neutral distribution of the cash flow is known to the traders, who make prices with a fixed multiplicative bid-offer spread and report their prices to a game master who declares that a trade has been made when the bid price of one of the traders crosses the offer price of the other. We prove that the value of the first trader's position is strictly greater than that of the second. The results are analyzed by use of simulation studies and generalized to situations where (a) there is a hierarchy of traders, (b) there are multiple successive trades, and (c) there is inventory aversion.

Jin Zhang, Xueqin Song, Lei Zhang et al.

Abstract Selecting superior clones is the first step for commercial short‐rotation coppice cultures to provide biomass and bioenergy. Till date, such selection for hybrid Populus clones in middle China is absent. Here we describe the growth, aboveground biomass production and cell wall composition of 27 hybrid poplar clones in Henan, China for two 3‐year rotations. Significant variation in these three characteristics over two triennial rotation coppices among the 27 poplar clones was observed. During two 3‐year rotation coppices, clones ‘276’, ‘02‐17’, and ‘599’ showed relatively higher tree heights and larger basal diameters than those of the other clones. However, the most productive clones were ‘36’ and ‘01‐30’. At the end of the second triennial rotation, the aboveground biomass production reached 18 Mg ha−1 year−1. For the cell wall composition analysis, the cellulose contents of clones ‘01‐243’ and ‘2001’ were relatively high, while the xylose contents of clones ‘01‐30’ and ‘65’ were relatively high. Cluster analysis based on height, basal diameter, biomass, heat value, cellulose content, and survival rate revealed five growth potential classes. Accordingly, clones ‘03‐332’, ‘36’, and ‘599’ exhibited high biomass and growth and had the greatest potential to serve as excellent biomass producers in Henan, China. In addition, the expression patterns of 20 key regulatory genes were analyzed, and an integrated coexpression network was constructed. This field trial provides a comprehensive quantification and evaluation of the agronomic performance of 27 poplar clones in Henan, China. The results of this study and the analytical strategies provide an efficient mechanism for selecting clones that will perform well agronomically in local environments. The expression of key genes and the integrated coexpression network provide the molecular mechanisms of poplar biomass performance.

Somayeh Ahmadi, Amir hossien Fakehi, Ali vakili et al.

In this study, a hybrid stochastic model (BScA model) using Bayesian approach and scenario analysis to forecast long term energy demand is developed. The main objective of this study is to design and develop a model for energy analysis in demand side and describe the energy saving and GHG reduction potential on the other. For this, total energy demand is selected as the response variable and primary energy production, population, GDP and natural gas and gasoline prices are chosen as covariates. Also, Political drivers, economic drivers, social-environmental and technological drivers are the key driving forces for scenario development. After interview and ranking the drivers, we have built scenario matrixes and reducing them upon strengths, weaknesses, opportunities and how the energy system perspective in each of the scenarios develop. Results show that primary energy production and population growth have positive impact on energy demand. And Energy consumption would decrease with energy price increase. And, economic development would rise energy demand. Also, the total potential for energy saving is equal to 3663 MBOE in duration of 2016–2040. Results demonstrate the energy intensity (EI) will be 2.12 MBOE/Million Rials in 2040 if energy saving solutions are taken. And, the carbon emission will reduce about 32% in 2040. Keywords: Bayesian, Energy demand, Scenario analysis, Hybrid stochastic model, Energy intensity, Energy saving